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Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general--all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:
- Collaborative filtering techniques that enable online retailers to recommend products or media
- Methods of clustering to detect groups of similar items in a large dataset
- Search engine features--crawlers, indexers, query engines, and the PageRank algorithm
- Optimization algorithms that search millions of possible solutions to a problem and choose the best one
- Bayesian filtering, used in spam filters for classifying documents based on word types and other features
- Using decision trees not only to make predictions, but to model the way decisions are made
- Predicting numerical values rather than classifications to build price models
- Support vector machines to match people in online dating sites
- Non-negative matrix factorization to find the independent features in adataset
- Evolving intelligence for problem solving--how a computer develops its skill by improving its own code the more it plays a game
"Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."
-- Dan Russell, Google
"Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."
-- Tim Wolters, CTO, Collective Intellect
- Sales Rank: #86785 in Books
- Brand: Segaran, Toby
- Published on: 2007-08-26
- Released on: 2007-08-23
- Original language: English
- Number of items: 1
- Dimensions: 9.19" h x .90" w x 7.00" l, 1.30 pounds
- Binding: Paperback
- 362 pages
About the Author
Toby Segaran is a software developer and manager at Genstruct, a computational systems biology company. He has written free web applications for his own use and put them online for others to try, including: tasktoy, a task management system; Lazybase, an online application that lets users design, create and share databases of anything they like; and Rosetta Blog, an online tool for practicing Spanish and French by reading blogs along with their translations and lists of common words. Each of these has several hundred regular users.
Most helpful customer reviews
111 of 115 people found the following review helpful.
Outdated
By Kyle
This is the first time I've actually taken the time to write out a review. I'm sure this book was awesome when it first came out, it is clear, concise and has a nice follow-along structure. However, it has become outdated and it is riddled with either old syntax and errors. I have gotten past most of that though. The worst part is probably that the files that are used in some of the examples are hosted on the authors blog and have been taken down. If he can't be bothered to continue hosting old files for people who may buy the book (or point us to somewhere to get them) we shouldn't be bothered to buy it.
13 of 13 people found the following review helpful.
Good - If you know what you're getting
By Denny
Let me start with pointing out what this book does well:
- The book gives a good survey of common Machine Learning algorithms. It explains what kind of problems these algorithms are good for. That's perfect for someone who wants to get a quick overview and has no background in Machine Learning.
- The book is very easy to understand. The writing style is very casual. Even people without formal training in Computer Science should have no problem. The only thing that's required is basic programming knowledge, preferably in Python.
- Among all of the theoretical ML books out there it's refreshing to find a book that applies the algorithms to real-world problems.
Now the negative points. The following are not necessarily negatives for everyone, as it really depends on what you were looking for in this book. However, I was expecting a bit more, and was disappointed about the following:
- Half of the books is code. I just don't see the point in printing full listing of Python code. Why not give shorter pseudocode and make the Python code available on the website? The long code listings only obfuscate the ideas instead of demonstrating how to apply them. If you take away the code listings there are maybe 150 pages of "real" content left.
- The very casual and easy-to-understand style comes at a price. The book does not go into the mathematical details of any of the algorithms. I understand that this wasn't the books intention to begin with, but I would argue that some mathematical background is necessary in order to efficiently apply complex algorithms. If you want to apply the algorithms presented in the book to slightly different or more complex problems, or wish to understand the advantages/disadvantages of each of the algorithms you'll have to know the basic math behind them.
- The algorithms are very poorly implemented. Looking at some of the code makes me cringe. While the code in the book may work for "Building a search engine" for a few thousand pages, or optimizing problems with a handful of variables, it certainly won't work for more interesting problems that involve real-world data, which is orders of magnitude larger. And the real-world scale is where these algorithms actually become interesting. The code in this book will only work for small examples where efficiency play no role. I understand the author wanted to keep the code as simple as possible, but in my opinion a few notes about how algorithms can be made more efficient would have been necessary. I can see many people trying to apply these algorithms to their real-world data, and getting stuck because of the poor implementation.
18 of 19 people found the following review helpful.
Outdates, Code no longer works
By albenali
I would not recommend anyone buying this book. The examples are outdated, based on sites that don't even exist anymore. The list of errata is huge (and doesn't even include all the errata I found).
Finally, the the author references old versions of python libraries that no longer 'work as advertised', or that have changed so radically that the example code will not even run - you'll be spending hours just figuring out how to update the code to be compatible with newer versions of the libraries referenced in the book. That in itself is a frustrating experience.
My advice is, get another book for Python or ML if that's what you're looking for and don't waste your time with this one.
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